Presence of noise is an inevitable part of image capturing. In the context of imaging and photography, noise refers to unwanted/undesirable spurious and extraneous information in the form of high frequency components present in an image. The manifestation of image noise due to the presence of high frequency components in out of focus regions of the image is rather more perceptually annoying and appears totally out of place.
This noise is introduced in the image by various components of the imaging system including optics, sensor, and post capture image processing. The process of creating an image with reduced amount of noise is termed as the denoising which has been a significant problem for last few decades and has attracted significant attention from both the academia and the industry. A major challenge in denoising process is determining whether the actual differences in pixel values constitute noise or real photographic/image details owing to the fact that both noise and image details are high frequency.
Many existing solutions for image denoising rely on either using (a) single image, or (b) multiple images. At an abstract level, multi-image solution first aligns the images and then takes a weighted average of the images. On the other hand, solutions using single image rely largely on filtering the images. While the filters are designed to adaptively smooth the images while trying to preserve the image details, these filters still lead to the loss of image details due to the image details being filtered out in addition to the noise.
In view of the above discussion it should be appreciated that there exist a need for improved methods and apparatus for removing and/or reducing image noise.